Weakly-supervised semantic segmentation aims to assign category labels to each pixel using weak annotations, significantly reducing manual annotation costs. Although existing methods have achieved remarkable progress in well-lit scenarios, their performance significantly degrades in low-light environments due to two fundamental limitations: severe image quality degradation (e.g., low contrast, noise, and color distortion) and the inherent constraints of weak supervision. These factors collectively lead to unreliable class activation maps and semantically ambiguous pseudo-labels, ultimately compromising the model's ability to learn discriminative feature representations. To address these problems, we propose Diffusion-Guided Knowledge Distillation for Weakly-Supervised Low-light Semantic Segmentation (DGKD-WLSS), a novel framework that synergistically combines Diffusion-Guided Knowledge Distillation (DGKD) with Depth-Guided Feature Fusion (DGF2). DGKD aligns normal-light and low-light features via diffusion-based denoising and knowledge distillation, while DGF2 integrates depth maps as illumination-invariant geometric priors to enhance structural feature learning. Extensive experiments demonstrate the effectiveness of DGKD-WLSS, which achieves state-of-the-art performance in weakly supervised semantic segmentation tasks under low-light conditions. The source codes have been released at: https://github.com/ChunyanWang1/DGKD-WLSS
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Few-Shot Segmentation(FSS) aims to efficient segmentation of new objects with few labeled samples. However, its performance significantly degrades when domain discrepancies exist between training and deployment. Cross-Domain Few-Shot Segmentation(CD-FSS) is proposed to mitigate such performance degradation. Current CD-FSS methods primarily sought to develop segmentation models on a source domain capable of cross-domain generalization. However, driven by escalating concerns over data privacy and the imperative to minimize data transfer and training expenses, the development of source-free CD-FSS approaches has become essential. In this work, we propose a source-free CD-FSS method that leverages both textual and visual information to facilitate target domain task adaptation without requiring source domain data. Specifically, we first append Task-Specific Attention Adapters (TSAA) to the feature pyramid of a pretrained backbone, which adapt multi-level features extracted from the shared pre-trained backbone to the target task. Then, the parameters of the TSAA are trained through a Visual-Visual Embedding Alignment (VVEA) module and a Text-Visual Embedding Alignment (TVEA) module. The VVEA module utilizes global-local visual features to align image features across different views, while the TVEA module leverages textual priors from pre-aligned multi-modal features (e.g., from CLIP) to guide cross-modal adaptation. By combining the outputs of these modules through dense comparison operations and subsequent fusion via skip connections, our method produces refined prediction masks. Under both 1-shot and 5-shot settings, the proposed approach achieves average segmentation accuracy improvements of 2.18% and 4.11%, respectively, across four cross-domain datasets, significantly outperforming state-of-the-art CD-FSS methods. Code are available at https://github.com/ljm198134/TVGTANet.
Existing open-vocabulary 3D semantic segmentation methods typically supervise 3D segmentation model by merging text-aligned features (e.g., CLIP) extracted from multi-view images onto 3D points. However, such approaches treat multi-view images merely as intermediaries for transferring open-vocabulary information, overlooking their rich semantic content and cross-view correspondences, and thus limiting the model's effectiveness. To this end, we propose PGOV3D, a novel framework that introduces Partialto-Global curriculum to improve Open-Vocabulary 3D semantic segmentation. The key innovation of our work is a two-stage training strategy. In the first stage, we pre-train the model on partial scenes that provide dense semantic information but relatively simple geometry. Partial point clouds are derived from multi-view RGB-D inputs via pixel-wise depth projection. To enable openvocabulary learning, we leverage a multi-modality large language model (MLLM) and a 2D segmentation foundation model to generate open-vocabulary labels for each viewpoint, providing rich and aligned supervision. An auxiliary inter-frame consistency module is introduced during this stage to enforce feature consistency under viewpoint variations and enhance spatial understanding. In the second stage, we fine-tune the model on complete scene-level point clouds, which are sparser and structurally more complex. To support this, we aggregate the partial vocabularies associated with each scene and generate pseudo labels using the pre-trained model, effectively bridging the semantic gap between dense partial observations and large-scale 3D environments. Extensive experiments on ScanNet, ScanNet200 and S3DIS benchmarks demonstrate that PGOV3D, achieves competitive performance in open-vocabulary 3D semantic segmentation. The code will be released.
While large language models (LLMs) have advanced procedural planning for embodied AI systems through strong reasoning abilities, the integration of multimodal inputs and counterfactual reasoning remains underexplored. To tackle these challenges, we introduce LLaPa, a vision-language model framework designed for multimodal procedural planning. LLaPa generates executable action sequences from textual task descriptions and visual environmental images using vision-language models (VLMs). Furthermore, we enhance LLaPa with two auxiliary modules to improve procedural planning. The first module, the Task-Environment Reranker (TER), leverages task-oriented segmentation to create a task-sensitive feature space, aligning textual descriptions with visual environments and emphasizing critical regions for procedural execution. The second module, the Counterfactual Activities Retriever (CAR), identifies and emphasizes potential counterfactual conditions, enhancing the model's reasoning capability in counterfactual scenarios. Extensive experiments on ActPlan-1K and ALFRED benchmarks demonstrate that LLaPa generates higher-quality plans with superior LCS and correctness, outperforming advanced models. The code and models are available https://github.com/sunshibo1234/LLaPa.
Efficient and precise open-vocabulary 3D scene segmentation remains a critical challenge in computer vision. While current leading methods encode CLIP language features into 3D Gaussians to achieve high segmentation accuracy and fast inference speeds, they suffer from point ambiguity issues caused by separately training on multi-level 2D semantic masks. This approach not only compromises time and space efficiency but also degrades accuracy when selecting optimal semantic levels. To overcome these limitations, we propose Voxel-Aware Fusion Language Gaussian Splatting (VaF-LangSplat), a novel framework that jointly optimizes geometric and semantic representations. Our approach first voxelizes 3D Gaussians using sparse point clouds and lightweight MLP decoders, effectively disentangling language features from geometric attributes. This enables simultaneous training across arbitrary semantic levels with minimal overhead. Crucially, we introduce Fusion Language Splatting, which aligns geometric and multi-level semantic distributions to sharpen boundary definitions while eliminating redundant Gaussian expansions. The voxel-aware representation further enhances robustness against motion blur and lighting variations. Experiments on open-vocabulary 3D localization and segmentation tasks demonstrate that VaF-LangSplat outperforms LangSplat (the prior state-of-the-art) with significant improvements in both segmentation/localization accuracy and efficiency: 4X faster training and 15X reduced storage requirements.
Medical image segmentation is essential for precise anatomical delineation and clinical decision-making. However, fully supervised methods are limited by the substantial cost of acquiring pixel-level annotations, particularly for 3D volumetric data. Semi-supervised learning (SSL) alleviates this challenge by leveraging unlabeled data, yet it remains hindered by severe class imbalance, where dominant structures disproportionately occupy the voxel space, leading to feature degradation and unreliable pseudo-labels. To address this issue, we propose a simple but effective SSL framework, namely Sub-Volume Contrastive Learning (SuVCL), to enhance feature discriminability in imbalanced 3D medical image segmentation. Our approach incorporates localized contrastive learning through sub-volume sampling, which captures small but semantically informative regions to retain fine-grained structural details while mitigating computational overhead. Furthermore, we introduce a balanced memory bank mechanism, which dynamically maintains class-specific feature representations with adaptive updates guided by class-predictive confidence. Extensive experimental evaluations demonstrate that our method substantially enhances segmentation performance for minority classes, demonstrating substantial performance gains over existing SOTAs.
Deep learning-based medical image segmentation, with its precise lesion localization capabilities, serves as a core component in multimedia medical applications and intelligent diagnostic assistance systems. Recent innovations in network architectures significantly improve segmentation performance. However, the Non-Effective Samples (NES) on model optimization receive little attention. These samples are characterized by minimal gradient variations in loss during training and exhibit a slight contribution to model optimization. They encompass well-segmented samples with near-zero loss values and challenging samples with consistently high loss values. Especially, when NES accumulate, the model will fall into an optimization trap, causing the optimization to stagnate. To address this issue, we propose a lightweight plug-and-play Gradient-Aware Sample Selection and Reactivation Strategy (GA-SRS) that efficiently identifies and revitalizes the training potential of NES. Firstly, GA-SRS filters NES out based on the historical training information of samples and the variations in the loss gradient during the training process. Then, GA-SRS revitalizes the training values of these samples through strong data augmentation. Extensive experiments on four public datasets and three general models demonstrate the effectiveness of GA-SRS. For example, GA-SRS helps improve the IoU metric of U-KAN from 67.20% to 71.24% on the BUSI and from 81.20% to 82.51% on the ISIC dataset, achieving state-of-the-art experimental results.
Class incremental medical segmentation (CIMS) aims to sequentially learn new classes while preserving knowledge of previously learned categories in the absence of old-class labels. Current methods suffer from performance degradation under class imbalance and require additional segmentation heads to accommodate new categories. Inspired by recent prototype learning that leverages prototypes to achieve robust recognition of new categories under limited-data regimes, we introduce a Prototype-dRIven class increMEntal (PRIME) method. PRIME replaces the incremental segmentation heads with prototypes to mitigate class imbalance, allowing new class learning with the simple addition of new prototypes. Based on prototype learning, PRIME further involves three tailored techniques. First, prototype structure alignment imposes structural constraints on inter-prototype relations to maintain consistent relative distances in the feature space, improving the model's ability to distinguish distinct classes. Second, pixel-wise contrastive loss term groups embeddings of similar samples while separating those of different classes, enhancing segmentation accuracy across all categories. Finally, the consensus-based prototype update mechanism refines the old prototypes during the learning of new classes, preventing performance degradation on the old classes. Extensive experiments on two public multi-organ segmentation datasets demonstrate that our approach significantly outperforms state-of-the-art methods, validating the effectiveness of the proposed PRIME.
Social group detection aims to identify groups of individuals exhibiting social behavior from multi-individual trajectory data. Recent approaches often determine group correlations based on global trajectory similarity, while temporal dynamics can cause diverging member trajectories and undermine similarity-based measures. Other methods model pairwise interaction strengths to capture group relations, focusing only on explicit direct interactions while ignoring implicit indirect interactions. To address temporal variability of group structures, we decompose long trajectories into multiple semantic sub-trajectories, enabling the capture of dynamic characteristics. Furthermore, to explore implicit indirect interactions, we introduce a unified spatio-temporal graph structure that models both direct and indirect interactions among individuals. In addition, considering the contextual influence of the neighborhood of an individual, we incorporate neighborhood information into the trajectory representation process. Based on these insights, we propose a Segmented-Trajectory-Aware Spatio-Temporal Graph Convolutional Network (SegTraj). This framework uniformly models explicit and implicit interactions through a spatio-temporal graph, and fuses individual trajectories with contextual neighborhood information for fine-grained representation of group relationships. Extensive experiments on three datasets covering both synthetic and real-world scenarios demonstrate that SegTraj significantly outperforms baseline methods. The code is available at https://github.com/DC0827/SegTraj.
Existing Unsupervised Video Object Segmentation (UVOS) solutions primarily focus on frame-to-frame propagation and often struggle with extended sequences where objects undergo complex transformations. In this work, we observe that many natural and artificial motions exhibit inherent periodicity, where objects return to similar states across time, particularly in complex scenarios. Leveraging this insight, we present PeriodVOS, a novel framework that exploits recurring motion patterns to enhance segmentation quality across diverse video contexts. Specifically, we propose to establish intra-period consistency to enforce stable segmentation within short time windows, while mitigating the effects of temporary disturbances. Furthermore, to capture global dependencies, we present inter-period correlation to build associations between similar object states across different time periods. Additionally, an adaptive temporal contextual coupling is designed to dynamically adjust how temporal context is integrated based on video content. Through extensive evaluation on six standard benchmarks, including DAVIS-2016, FBMS, Youtube-Objects, DAVSOD, ViSal, and MCL datasets, our PeriodVOS outperforms state-of-thearts, demonstrating the potential of video periodic mining particularly in challenging scenarios. We have released source code on https://github.com/smdshzyjbr-qhw/PeriodVOS.
Medical image segmentation is crucial for clinical decision-making, treatment planning, and disease tracking. Nonetheless, it confronts two significant challenges: the presence of ''soft boundaries'' between the foreground and background exacerbated by poor illumination and low contrast, and the misleading co-occurrence of salient and non-salient objects during the training phase, which complicates the model's accuracy in distinguishing relevant features. To overcome these challenges, we introduce RoDeCon-Net, a novel framework engineered to enhance medical image segmentation. RoDeCon-Net incorporates a Feature Decoupling Unit (FDU) that dynamically separates encoded features into foreground, background, and uncertain regions, using advanced attention mechanisms to refine feature distinction and reduce uncertainty. Additionally, our Contrast-driven Feature Alignment Unit (CFAU) and Cross-layer Feature Cascade Unit (CFCU) synergize to reinforce feature contrasts and promote effective multi-level feature fusion, thus improving the detection of salient objects amidst complex backgrounds and handling various object scales within images. Comprehensive evaluations of RoDeCon-Net on five diverse medical image datasets validate its superior performance and versatility, showcasing its potential to set new benchmarks in medical image segmentation. Our code is available on https://github.com/ILoveACM-MM/RoDeCon-Net.
Camouflaged Object Detection (COD) aims to identify objects that blend seamlessly into their surroundings. The inherent visual complexity of camouflaged objects, including their low contrast with the background, diverse textures, and subtle appearance variations, often obscures semantic cues, making accurate segmentation highly challenging. Existing methods primarily rely on visual features, which are insufficient to handle the variability and intricacy of camouflaged objects, leading to unstable object perception capability and ambiguous segmentation results. To tackle these limitations, we introduce a novel COD task, class-guided camouflaged object detection (CGCOD), which extends the conventional COD task by incorporating object-specific class knowledge to enhance detection robustness and accuracy. To facilitate this task, we present a new dataset, CamoClass, comprising camouflaged objects with class annotations. Furthermore, we propose a multi-stage framework, CGNet, which incorporates a plug-and-play class prompt generator and a simple yet effective class-guided detector. This establishes a new paradigm for COD, bridging the gap between contextual understanding and class-guided detection. Extensive experimental results demonstrate the effectiveness of our flexible framework in improving the performance of proposed and existing detectors by leveraging class-level textual information. The Camoclass dataset and the corresponding source code will be made publicly available upon acceptance at: https://github.com/bbdjj/CGCOD.
Medical image segmentation plays an important role in clinical decision making and auxiliary diagnosis. Today, however, it still faces three major challenges. 1. In the task of medical image segmentation, due to the different types of lesions and the large difference in the size of the lesion area, the segmentation accuracy is seriously reduced. 2. In order to pursue the segmentation performance, the model is difficult to be applied to the actual medical environment due to the excessive parameters. 3. Relying too much on manually labeled images to assist training. In order to meet these challenges, we propose a lightweight segmentation network, which is dedicated to extracting local and global information and fusing multi-level and multi-source features to maximize the segmentation accuracy for different shape lesions, especially for the case of fuzzy boundary and small segmentation target. The method of generating intermediate mask self-monitoring is used to generate additional labeled images to assist training. Finally, by using efficient down sampling and up sampling operations, the parameter quantity is only 1.37M while effectively extracting information. On the BUSI and ISIC2018 datasets, mIoU and DSC scores reached 75.57%, 83.57% and 83.85%, 90.38% respectively, indicating that we have reached the best balance between parameters and performance. The code is available at https://github.com/Jay217219/EMIFS.
Unlike popular solutions based on dense feature maps, Object-Centric Learning (OCL) represents visual scenes as sub-symbolic object-level feature vectors, termed slots, which are highly versatile for tasks involving visual modalities. OCL typically aggregates object superpixels into slots by iteratively applying competitive cross attention, known as Slot Attention, with the slots as the query. However, once initialized, these slots are reused naively, causing redundant slots to compete with informative ones for representing objects. This often results in objects being erroneously segmented into parts. Additionally, mainstream methods derive supervision signals solely from decoding slots into the input's reconstruction, overlooking potential supervision based on internal information. To address these issues, we propose Slot Attention with re-Initialization and self-Distillation (DIAS): i) We reduce redundancy in the aggregated slots and re-initialize extra aggregation to update the remaining slots; ii) We drive the bad attention map at the first aggregation iteration to approximate the good at the last iteration to enable self-distillation. Experiments demonstrate that DIAS achieves state-of-the-art on OCL tasks like object discovery and recognition, while also improving advanced visual prediction and reasoning. Our source code and model checkpoints are available on https://github.com/Genera1Z/DIAS.
Current medical vision-language pre-training models primarily follow two paradigms: report-supervised cross-modal alignment pre-training and reconstruction-based self-supervised pre-training. The former enhances the discriminative power of representations, while the latter facilitates fine-grained representation learning. However, naively combining these two paradigms inherits their inherent limitations: reconstruction-based methods treat all image patches equally during reconstruction, failing to effectively capture critical pathological details-since disease-related regions typically occupy only a small fraction of the image. Meanwhile, alignment-based methods suffer from suboptimal representations due to the presence of false negatives. To address these challenges, we propose a novel pre-training framework that integrates two key components: Pathology-Aware Reconstruction (PAR) and Discriminative Knowledge-Boosted Alignment (DKBA). Through a cascaded training strategy, our framework effectively combines the strengths of both paradigms while mitigating their inherent limitations. During the reconstruction pre-training stage, PAR incorporates pathology-aware priors to enhance the model's ability to capture fine-grained pathological details. In the alignment pre-training stage, DKBA leverages a medical knowledge graph as external supervision to improve cross-modal clustering alignment, thereby reducing the negative impact of false negatives. Extensive experiments on diverse downstream medical imaging tasks including image classification, object detection, and semantic segmentation, demonstrate the superior generalization capabilities of our method. Our code is publicly available at https://github.com/Felix1118/PADKB.
SAM-TTT: Segment Anything Model via Reverse Parameter Configuration and Test-Time Training for Camouflaged Object Detection
PDF ↗This paper introduces a new Segment Anything Model (SAM) that leverages reverse parameter configuration and test-time training to enhance its performance on Camouflaged Object Detection (COD), named SAM-TTT. While most existing SAM-based COD models primarily focus on enhancing SAM by extracting favorable features and amplifying its advantageous parameters, a crucial gap is identified: insufficient attention to adverse parameters that impair SAM's semantic understanding in downstream tasks. To tackle this issue, the Reverse SAM Parameter Configuration Module is proposed to effectively mitigate the influence of adverse parameters in a train-free manner by configuring SAM's parameters. Building on this foundation, the T-Visioner Module is unveiled to strengthen advantageous parameters by integrating Test-Time Training layers, originally developed for language tasks, into vision tasks. Test-Time Training layers represent a new class of sequence modeling layers characterized by linear complexity and an expressive hidden state. By integrating two modules, SAM-TTT simultaneously suppresses adverse parameters while reinforcing advantageous ones, significantly improving SAM's semantic understanding in COD task. Our experimental results on various COD benchmarks demonstrate that the proposed approach achieves state-of-the-art performance, setting a new benchmark in the field. The code will be available at https://github.com/guobaoxiao/SAM-TTT.
DisFaceRep: Representation Disentanglement for Co-occurring Facial Components in Weakly Supervised Face Parsing
PDF ↗Face parsing aims to segment facial images into key components such as eyes, lips, and eyebrows. While existing methods rely on dense pixel-level annotations, such annotations are expensive and labor-intensive to obtain. To reduce annotation cost, we introduce Weakly Supervised Face Parsing (WSFP), a new task setting that performs dense facial component segmentation using only weak supervision, such as image-level labels and natural language descriptions. WSFP introduces unique challenges due to the high co-occurrence and visual similarity of facial components, which lead to ambiguous activations and degraded parsing performance. To address this, we propose DisFaceRep, a representation disentanglement framework designed to separate co-occurring facial components through both explicit and implicit mechanisms. Specifically, we introduce a co-occurring component disentanglement strategy to explicitly reduce dataset-level bias, and a text-guided component disentanglement loss to guide component separation using language supervision implicitly. Extensive experiments on CelebAMask-HQ, LaPa, and Helen demonstrate the difficulty of WSFP and the effectiveness of DisFaceRep, which significantly outperforms existing weakly supervised semantic segmentation methods. The code will be released at https://github.com/CVI-SZU/DisFaceRep.
Recently, query-based methods have achieved remarkable performance in Referring Video Object Segmentation (RVOS) by using textual static object queries to drive cross-modal alignment. However, these static queries are easily misled by distractors with similar appearance or motion, resulting in query selection bias. To address this issue, we propose Triple Query Former (TQF), which factorizes the referring query into three specialized components: an appearance query for static attributes, an intra-frame interaction query for spatial relations, and an inter-frame motion query for temporal association. Instead of relying solely on textual embeddings, our queries are dynamically constructed by integrating both linguistic cues and visual guidance. Furthermore, we introduce two motion-aware aggregation modules that enhance object token representations: Intra-frame Interaction Aggregation incorporates position-aware interactions among objects within a single frame, while Inter-frame Motion Aggregation leverages trajectory-guided alignment across frames to ensure temporal coherence. Extensive experiments on multiple RVOS benchmarks demonstrate the advantages of TQF and the effectiveness of our structured query design and motion-aware aggregation modules. The code is available at https://github.com/DavidZhang-1025/TQF.
Large Vision Language Models (LVLMs) have been widely adopted to guide vision foundation models in performing reasoning segmentation tasks, achieving impressive performance. However, the substantial computational overhead associated with LVLMs presents a new challenge. The primary source of this computational cost arises from processing hundreds of image tokens. Therefore, an effective strategy to mitigate such overhead is to reduce the number of image tokens-a process known as image token pruning. Previous studies on image token pruning for LVLMs have primarily focused on high-level visual understanding tasks, such as visual question answering and image captioning. In contrast, guiding vision foundation models to generate accurate visual masks based on textual queries demands precise semantic and spatial reasoning capabilities. Consequently, pruning methods must carefully control individual image tokens throughout the LVLM reasoning process. Our empirical analysis reveals that existing methods struggle to adequately balance reductions in computational overhead with the necessity to maintain high segmentation accuracy. In this work, we propose LVLM_CSP, a novel training-free visual token pruning method specifically designed for LVLM-based reasoning segmentation tasks. LVLM_CSP consists of three stages: clustering, scattering, and pruning. Initially, the LVLM performs coarse-grained visual reasoning using a subset of selected image tokens. Next, fine-grained reasoning is conducted, and finally, most visual tokens are pruned in the last stage. Extensive experiments demonstrate that LVLM_CSP achieves a 65% reduction in image token inference FLOPs with virtually no accuracy degradation, and a 70% reduction with only a minor 1% drop in accuracy on the 7B LVLM.
Visual language navigation (VLN) poses challenges in guiding agents through unseen environments based on natural language instructions. Existing methods either rely on imitation learning, for which training across various complex scenarios remains challenging, or leverage large visual language models (LVLMs) for zero-shot object recognition and expert iterative reasoning for improved scene understanding. Although LVLMs enhance target detection generalization, current VLN methods lack robustness in terms of environmental generalization and struggle with multi-step, coarsely directed instructions. Addressing these challenges, we introduce Ali-UI, a novel vision-language navigation approach that enables agents to navigate from random starting points in unvisited scenes and handle complex multi-step instructions. Specifically, we incorporate continuously accumulating global grid maps and local semantic maps as scene memory by employing frontier-based exploration. Multi-step coarsely directed commands are broken down with the assistance of LLaVA and matched with the scene, considering temporal and spatial alignment. Panoramic data are saved in topological form and queried by instruction segments for sequential navigation. Extensive experiments carried out in simulated environments demonstrate that Ali-UI outperforms existing state-of-the-art methods in terms of flexible human instructions and scene generalization, with the success rate improved by 23.37% and the SPL increased by 19.27% in R2R dataset.